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dc.contributor.authorTatli, Ali
dc.contributor.authorKahvecioğlu, Sinem
dc.date.accessioned2019-10-22T20:07:10Z
dc.date.available2019-10-22T20:07:10Z
dc.date.issued2016
dc.identifier.isbn978-605-01-0923-8
dc.identifier.urihttps://hdl.handle.net/11421/22235
dc.descriptionNational Conference on Electrical, Electronics and Biomedical Engineering (ELECO) -- DEC 01-03, 2016 -- Bursa, TURKEYen_US
dc.descriptionWOS: 000401519800026en_US
dc.description.abstractAviation, a serious sector, is required to recourse planning intensively. There is a need for foresight in order to eliminate the planless that can lead to high costs and prevent accidents that could cause to fatal consequences. Without doubt, considering inexperienced pilot candidates this situation comes into prominence for profit-oriented flight training organizations. This study contains extracting time series of airworthiness and forecasting amount of daily airworthiness time based on NARX (Nonlineer Auto Regressive Exogenous) neural network models.en_US
dc.description.sponsorshipUludag Univ, Muhendislik Fakultesi, Elektrik Elektronik Muhendisligi Bolumu, Istanbul Teknik Univ, Elektrik Elektronik Fakultesi, TMMOB Elektrik Muhendisleri Odasi Bursa Subesien_US
dc.language.isoturen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNarx Networksen_US
dc.subjectAirworthinessen_US
dc.subjectTime Series Predictionen_US
dc.subjectArtificial Neural Networks Performanceen_US
dc.titleNARX Neural Networks Based Time Series Prediction for Amount of Airworthiness Timeen_US
dc.typeconferenceObjecten_US
dc.relation.journal2016 National Conference On Electrical, Electronics and Biomedical Engineering (Eleco)en_US
dc.contributor.departmentAnadolu Üniversitesi, Sivil Havacılık Yüksekokuluen_US
dc.identifier.startpage130en_US
dc.identifier.endpage134en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US]


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